Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230200146-6.doi: 10.11896/jsjkx.230200146

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Rail Light Band Detection Algorithm Based on Deep Learning

ZHANG Xinfeng1, BIAN Haonan1, ZHANG Bo2, ZHANG Jiaming1, LIANG Yuqing1   

  1. 1 Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China
    2 Institute of Infrastructure Testing,China Academy of Railway Sciences Group Co.,LTD.,Beijing 100081,China
  • Published:2023-11-09
  • About author:ZHANG Bo,born in 1988,Ph.D,asso-ciate researcher.His main research interests include inspection and monitoring of railway infrastructure,and so on.
  • Supported by:
    Foundation of China Academy of Railway Sciences Group Co. LTD.(2022YJ179).

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Abstract: When the train is running on the track,the rim of the wheel will crush the rail surface to form a light band.The shape of the light band reflects the position relationship between the rail and the wheel.The capture of the abnormal light band shape can effectively prevent the safety problems of the train operation and improve the comfort of the train.The traditional light band detection method uses manual detection,which has some problems such as low efficiency and strong professionalism.The early computer vision technology used the edge information and gray information of the image to locate the rail region,and then segmented the light band region on this basis,which was not satisfactory in efficiency and robustness.Therefore,it is necessary to segment the rail and the light band with high efficiency and high precision.This paper firstly uses ResNet classification network to classify the image of the non-turnout and the image of turnout.Then,for the two kinds of images,DeeplabV3+ segmentation network is used to segment the light band and rail area of the image respectively.Finally,aiming at the problem that the edge of the rail is easy to be segmented unclearly,this paper proposes a post-processing algorithm based on the Douglas-Peucker algorithm to fit the edge of the rail.The research results show that,compared with the direct use of semantic segmentation network for the segmentation of two types of images,the segmentation accuracy can be improved steadily through the classification operation and the post-processing of the segmentation results.In addition,the intersection over union(IOU) of the overall segmentation,rail segmentation and light band segmentation of the non-turnout images are 95.45%,87.48% and 92.60%,respectively.For turnout images,the values are 90.20%,76.56% and 85.42%,respectively.The proposed algorithm has high precision for the segmentation of rail and light band region,and can efficiently complete batch image processing,which has high engineering value.

Key words: Deep learning, Image processing, Damage detection, Semantic segmentation, Railway track

CLC Number: 

  • TP391
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